Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

3][4] Unfortunately, despite advances in treatment, MM remains incurable.Intratumor heterogeneity significantly challenges effective tumor response, prognosis, and survival. 5he traditional bulk-tissue analysis only provides a virtual average of the multiple cellular components.The conventional analysis of bulk tissue only provides an average representation of multiple cellular components.However, the emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled a comprehensive and unbiased examination of gene expression patterns and cell fate decisions at high resolution and depth for individual cells. 6,7For this reason, scRNA-seq has played a profound role in investigating the pathogenesis of MM and guiding clinical treatment.
In this review, we provide a comprehensive overview of the strengths and limitations of various single-cell sequencing platforms and analytical tools as well as delineate the experimental workflow for scRNA-seq in MM.Furthermore, we summarize the research progress using scRNA-seq that has substantially improved our experience with single-cell sequencing technologies in disease research and has also helped to improve our understanding of the underlying mechanisms of MM.

SINGLE-CELL RNA SEQUENCING TECHNIQUES
The process of single-cell RNA sequencing (scRNA-seq) involves a series of steps, which have been summarized in Figure 1.

Samples acquisition and preparation
Unlike several solid tumor samples 8,9 that require mechanical dissociation and enzymatic digestion, MM is a tumor of the BM and blood system, and samples obtained from the peripheral blood (PB) and BM aspirates do not require much complicated processing to produce cell suspensions.After that, the cell type required for sequencing is separated from mononuclear cells obtained by density gradient centrifugation using magnetic activated cell sorting (MACS) 10 or fluorescence-activated cell sorting. 11In conjunction with light scatter characteristics, the combination of CD38, CD138, and CD45 markers has emerged as the preferred approach for identifying and quantifying plasma cells.Furthermore, the utilization of CD19, CD56, CD117, CD20, CD28, CD27, and CD81 expressions alongside CyIg light-chain restriction is associated with distinct behavioral patterns that enable unequivocal differentiation between clonal and normal/reactive plasma cells. 12

Principle scRNA-seq pipeline
Once a certain concentration of cell suspension is obtained, various scRNA-seq protocols are available for sequencing.For scRNA-seq protocols, the key difference is whether the sequencing is a full-length transcript or 5ʹ/3ʹ-end fragment.Additionally, depending on the different cell capture strategies employed, scRNA-seq protocols can be categorized as either droplet-based or plate-based.Protocols with low cell capture efficiency may only be reliable for analyzing modest to abundant cell components. 13Protocols for RNA sequencing are briefly summarized in Table 1.Researchers can choose different protocols according to their research requirements.After that, the following steps are typically involved: capture mRNA from single-cell lysis; reverse transcribing the mRNA into cDNA and amplifying it through polymerase chain reaction (PCR) or in vitro transcription; library construction; and sequencing.To begin analyzing scRNA-seq data, the first step is to process the raw reads into feature-barcode matrices that can be used in downstream analyses.If the 10× genomics protocol 14 was used for library construction, CellRanger offers a convenient method for this processing, albeit slow and memory intensive.Alternative read processing approaches such as DropEst, Kallisto-BUStools, UMI-Tools, STARSolo, and Alevin are optimized for runtime and memory, allowing users to process their scRNA-seq runs without investing as much in computational infrastructure. 29Some downstream analytical tools are summarized in Table 2.

Quality control
Data quality can be impacted by technical artifacts associated with cell dissociation, cell encapsulation, library preparation, or sequencing processes. 78Cells that exhibit relatively small library sizes, low gene expression levels, or a high proportion of reads or unique molecular identifiers mapping to mitochondrial DNA (mtDNA)-encoded genes are considered to be of low quality. 34Prior to downstream analysis, it is necessary to eliminate low-quality cells and systematically evaluate technical artifacts. 79ost scRNA-seq protocols may encapsulate 2 or more cells and barcode in 1 reaction volume, and the data that represent multiplets can use doublet removal 42 package to remove.Existing tools such as miQC, 39 Scanpy, 30 Seurat, 35 and Scater 31 can be used to filter cells based on user-defined thresholds of these parameters.

Normalization and data correction and Integration
Normalization is the process of removing technical effects typically arise from cDNA capture or PCR amplification efficiency across cells while preserving true biological variation such as heterogeneity or differential expression. 43The normalization of scRNA-seq can be roughly divided into two broad categories.One is based on "size factors," such as BASICS corrected with spike-ins, 80 Scran, 34 which measures size factors with multiple "cell pools," and TMM 81 and DESeq, 82 which are traditionally used for bulk transcriptional standardization.The other is a probability distribution-based approach that fits specific parameters according to the expression distribution of each gene and then normalizes each gene, such as SCnorms, 45 SCTransform, 43 and ZINB adopted by scVI. 83A normalized measure is then used for downstream analysis, such as the detection of highly variable features, clustering and differential expression. 84Other detailed normalization methods comparison have been published previously. 43,85espite normalization, unintended variabilities such as batch, dropout, or cell cycle effects 86 may still occur.Many studies incorporate sequencing data from multiple experiments.These data generated using different reagents, handling protocols, equipment, or sequencing platforms result in batch effects. 46o date, the batch-effect removal methods have advantages and limitations, with no method clearly superior.Harmony, 49 however, performed better when analyzing large datasets and common cell types. 87Moreover, data correction arising from cell cycle effects can be corrected by performing a simple linear regression on cell cycle scores, implemented in both Scanpy 30 and Seurat 35 platforms.

Highly variable genes selection and dimensionality reduction
With a large number of cells being measured, single-cell datasets tend to have high dimensions and introduce noise and complex calculations into computational work.In homogeneous cell populations, highly variable genes (HVGs) are detected to identify genes with the strongest biological signal that significantly contribute to cell-to-cell variability.Some functions like "trendVar" and "decomposeVar" from the scran, 34 "FindVariableFeatures" from the Seurat, 35 "BrenneckeGetVariableGenes" from the M3Drop 88 can be used to select HVGs.Restricting downstream analyses to the most informative genes can mitigate the impact of dimensionality and noise, while simplifying the analysis. 89To further reduce computational complexity and negative effects associated with high-dimensional expression matrices, dimensionality reduction algorithms employing both linear and nonlinear methods have been developed.A comparison of various dimensionality reduction techniques has previously been published. 90The most commonly employed linear transformation strategies involves principal component analysis (PCA), which identifies axes (or PC) that capture the greatest amount of variation in high-dimensional space.Moreover, since PCA preserves meaningful information in early axes, the number of PCs selected for subsequent analysis affects the magnitude of relevant information loss or noise, which results in distortion of the basic pattern of variation/covariation. 91nlike PCA, nonlinear dimensionality reduction algorithms such as t-distributed stochastic neighbor embedding (t-SNE) 61 and uniform manifold approximation and projection (UMAP) 51 are not confined to linear transformation, offering greater flexibility in arranging cells in low-dimensional space.This allows for the separation of unique clusters within complex populations.Furthermore, UMAP is increasingly supplanting t-SNE as the preeminent method for dimensionality reduction due to its fast computation time, capacity to meaningfully represent extremely large datasets and retention of large-scale information. 51

Unsupervised clustering and cell-type annotation
Unsupervised clustering is a critical step in the visualization process of scRNA-seq data analysis, enabling identification of cell clusters with similar expression profiles.These clusters may represent distinct cell types or different stages of the same type.Unlike supervised clustering, which relies on predefined labels for cells, unsupervised clustering focuses solely on the data and employs algorithms to group cells.A variety of clustering methods have been developed for scRNA-seq analysis, including the widely used k-means algorithm based on minimizing the sum of squared Euclidean distances, 92 hierarchical clustering based on constructed branch of cell types and subtypes, 93 density-based clustering based on the density of datapoints in the input space, 94 and graph-based clustering assuming that dense communities in a graph can be represented as spectral components or dense subsets. 95Apart from these, there are also clustering algorithms such as mixture models, 96 neural networks, 97 ensemble clustering, 62 and affinity propagation. 98Their strengths, limitations, and time complexity are summarized in this article. 99,100nnotation theoretically compares cell expression profiles or specific marker genes in cell clusters with annotated databases.Annotation methods are mainly divided into 3 categories, namely marker gene database-based, correlation-based and annotation by supervised classification.SingleR 64 is a commonly used correlation-based annotation method, which labels cells based on the reference samples with the highest Spearman rank correlations, focusing on the significant distinctions across cell types by using just the marker genes between pairs of labels.Other automated cell-type annotation methods have been evaluated across a wide range of tissues, sample conditions, and applications. 101    (Continued) time-saving and more objective than manual annotation, it still requires supplementation and improvement through manual annotation in many cases.

Gene-level analysis
Differentially expressed genes (DEGs) analysis is a widely used approach to identify genes that exhibit differential expression between populations in both scRNA-seq and bulk RNA-seq experiments.However, the numerous DEG methods available vary greatly in terms of the number and characteristics of DEGs they detect, as well as their stability and potential biases.These factors are compared in this study. 102fter obtaining the DEGs, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases can be used for term or pathway enrichment analysis.Moreover, gene set enrichment analysis (GSEA) is a supervised method that assesses the statistical significance or concordance of predefined gene sets between two biological states.However, its application is typically limited to case/control experimental designs and requires prior analysis of sample differences. 67Superior to GSEA, gene set variation analysis is an unsupervised, nonparametric manner used for analyzing complex and highly heterogeneous pathway activity over a sample population. 68Nonetheless, because there are no gold standard expression datasets, the reproducibility and sensitivity of gene set analysis remain to be addressed. 103ene regulatory networks (GRNs) are frequently utilized to illustrate the combinations of active transcription factors that interact with a specific set of cis-regulatory regions in the genome, resulting in a unique gene expression profile for each cellular function or stable state. 104A systematic assessment of methods for inferring GRNs has been published. 105

Cell-level analysis
Pseudotime analysis reveals the regularity of the dynamic process of the cell, and the cells are arranged along the trajectory according to the differentiation expression during the process. 106The inferred pseudotime is a 1-dimensional coordinate assigned by trajectory and because all cells were collected at once, the notion of pseudotime does not represent real-time.Existing algorithms for trajectory inference are complementary and show optimal performance based on the characteristics of the data. 107elocity estimation employs the ratio of unsliced and sliced mature mRNA abundance to describe the rate of gene expression changes at a specific time point for individual genes. 76This approach overcomes the limitation of pseudotime analysis, which only provides static snapshots of cellular states and fails to capture dynamic changes in scRNA-seq data.Original velocity estimation model 76 requires a number of constraints such as all genes having the same rate of splicing or the presence of steady states.ScVelo, 75 however, is a likelihood-based dynamical model that expands RNA velocity estimation to transient and heterogeneous subpopulation kinetics systems.
Cell-cell communication (CCC) mediated by exchanging metabolites and ligand-receptor complexes is crucial to the development, differentiation, and function of cells. 108CCC can be inferred from scRNA-seq data at both the individual cell and cell cluster levels using computational methods.However, due to the occurrence of several cell contacts between adjacent cells and the lack of tissue spatial location information in scRNAseq, integrating it with spatial transcriptome may alleviate limitations in studying CCC. 109ompositional analysis requires sufficient cell and sample numbers to estimate cell-type proportion in a sample and evaluate expected background variation. 110Because of the limited sample sizes and compositionality of scRNA-seq data, existing models either require advanced cell clustering assignment or cannot avoid false univariate inferences due to negative correlations, do not adequately reflect the overall cell-type composition. 77,111

Clonal evolution patterns
MM is a neoplastic disease of plasma cells, characterized by the clonal proliferation of malignant plasma cells in the BM.The disease has a complex evolutionary process resulting from multiple genomic and molecular expression events and cellular heterogeneity in the BM microenvironment (BME).The clinical spectrum of the disease encompasses an asymptomatic stage, such as MGUS with suspected malignant plasma cells producing abnormal monoclonal antibodies (M-protein) in the blood, and a more advanced stage, SMM, characterized by a high proportion of malignant plasma cells in the BM and/or M-protein in the blood. 112Individuals with SMM, although asymptomatic, are genetically mature entity whereby molecularly indistinguishable from those with active MM. 113,114The genetic events in the progression from asymptomatic to MM can be broadly divided into 2 stages.The primary genetic events begin in a maturing B cell clone and are commonly divided into nonhyperdiploid (non-HRD) and HRD endotypes.The non-HRD tumors harbor immunoglobulin heavy chain (IGH) locus translocations, predominantly involving t(4;14), t(6;14), t(11;14), t(14;16), t(14;20) and deletion of 13q.HRD tumors refer to the presence of trisomy of chromosomes such as 3, 5, 7, 9, 11, 15, 19, and 21. 115In accordance with Darwinian evolution theory, genetic alterations that confer a fitness advantage to a clone over other populations are preserved under selective pressure from the tumor microenvironment such as immune surveillance, clonal competition, or drug treatment.Those genetic changes carried by these subclones of cells preserved under selective pressure may be considered driver mutations that play a significant role in tumor clonal evolution.7][118][119][120][121] Some of these mutations are found in a large proportion of patients or affect the pathways, such as KRAS and NRAS mutation in MAPK pathway, TP53, ATM, and ATR mutations in the DNA repair pathway, translocations or copy number variations of MYC, and potential tumor suppressor genes (DIS3 and FAM46C) may be regarded as the independent risk factors of disease progression. 115ith the implementation of scRNA-seq techniques, a deeper understanding of the transcriptional heterogeneity among individual cells has been unveiled.The advancements in scRNAseq research for MM have been summarized in Table 3 and Figure 2. One of the initial studies utilizing multi-omic scRNAseq and bulk DNA sequencing to examine longitudinal samples from 14 individuals at varying stages of disease, revealing patient-specific plasma cell profiles and immune cell expression variation.Unique subpopulations of plasma cells exhibit transcriptional stability during the progression from asymptomatic SMM to overt disease but demonstrate dynamic emergence or loss during the transition from newly diagnosed MM (NDMM) to RRMM. 1317][158] Additionally, the conventional diagnosis of MM involves BM aspiration, which is an invasive and painful procedure that does not facilitate early detection or follow-up monitoring.Lohr et al were the first to employ scRNA-seq to analyze CTCs and MM cells derived from BM, revealing that CTCs harbor identical genetic and transcriptomic information as primary BM cells. 154Geng et al and Pang et al  Table 3.
(Continued) that plasma cells (cPCs) abnormally auto-secrete CXCL12 and downregulate the expression of S1PR2 to help extramedullary translocation from the BM. 146,148n many cases of MM, the normal balance between bone resorption and new bone formation is disrupted, resulting in bone deterioration and the development of osteolytic lesions. 159Osteoclast activation-induced bone damage generally occurs around MM plasma cells rather than in normal BM. 160urthermore, PCs obtained from osteolytic lesions exhibited upregulation of genes associated with myeloma-related bone disease, including DKK1, HGF, and TIMP-1, as well as recurrent downregulation of JUN/FOS, DUSP1, and HBB. 1222][163][164] Previous meta-analyses and case reports have identified various risk factors associated with SPMs, whereas scRNA-seq can be utilized to uncover the underlying etiology and molecular mechanisms.
To sum up, based on single-cell analyses, it has been discovered that driver genetic alterations play a crucial role in maintaining the fitness of tumor cells.However, it is also important to note that tumor evolution is also influenced by phenotypic plasticity and interactions that take place within the BME.

Microenvironment
The BME is composed of both cellular and noncellular components.The cellular component contains hematopoietic cells encompassing B cells, T cells, natural killer (NK) cells, osteoclasts, and nonhematopoietic cells such as fibroblasts, osteoblasts, endothelial cells, and mesenchymal stromal cells (MSCs).The extracellular matrix, oxygen concentration, and the liquid milieu (cytokines, growth factors, and chemokines) constitute the noncellular compartment within the BME that is either generated or influenced by the cellular compartment. 165,166Bailur et al first applied the scRNA-seq technique to patients with MGUS and MM, showing a gradual increase in terminal effector T cells as the cancer progressed and describing the immune-suppressive phenotype constructed by tumor-infiltrating myeloid cells: reduced expression of CD86, CD155, and c-KIT and increased PD-L1. [151]Zavidij O et al obtained samples from MGUS, lowrisk SMM, high-risk SMM, MM and healthy donors and used MACS to isolate CD138 − or CD45 + cell fractions, demonstrated a significant predominance of NK cells, nonclassical monocytes and macrophages, and T cells in the asymptomatic stages. 143nterferon (IFN) type I secreted by MM cells promotes immunosuppression and favors MM growth, and the upregulation of IFN is already detectable at the SMM stage. 143Cho et al distinguished 4 clusters of NK cells by DEGs analysis, respectively adaptive NK, terminal NK, CD56 bright NK, and NK-HSP.The expression of CD38 in adaptive NK cells was lower than that in conventional NK cells, thus avoiding CD38 monoclonal antibody daratumumab-induced fratricide and increasing daratumumab efficacy by mediating ADCC to kill tumor cells. 140Li et al further divided NK cells into 7 subsets through unsupervised clustering, among which ZNF683 + NK subset was significantly increased in MM compared with healthy donors.ZNF683 + NK cells demonstrate exhaustion phenotypes characterized by higher expression of NK cell inhibitory markers LAG3 and www.blood-science.orgDu et al KIR3DL2 and lower expression of cytotoxic gene SH2D1B, granzyme gene GZMA and granulysin gene GNLY and can be a target for immunotherapy. 123n ND/RRMM patients, there is a group of immature BM progenitor cells known as myeloid-derived suppressor cells (MDSCs) that accumulate in both the BM and PB.8][169] They also can promote MM growth while inducing the generation of Treg cells and suppressing T-cell-mediated immune responses through secreting ARG1, ROS, COX2, iNOS, IL-6, and IL-10 and limiting the extracellular factors required for T-cell activation. 168,170There is no relevant study applying scRNA-seq to this highly heterogeneous population of MDSCs.The heterogeneity of MDSCs remains to be explored and can be a potential target for immunotherapy. 171urthermore, it has been demonstrated that the interactions between MM plasma cells and the BME play a critical role in regulating the dormancy of myeloma cells.Dormant myeloma cells may disseminate early in the disease course, acquire resistance to conventional treatments targeting proliferating cells, and persist as minimal residual disease (MRD), which can be reactivated to trigger disease relapse. 172Khoo et al identified a transcriptome signature of dormant MM cells in mice, characterized by the upregulation of Irf7, Spic, AXL, FCER1G, CSF1R, SIRPA, and VCAM1 genes. 149Further investigation into parallel dormancy genes in clinical samples from healthy donors and MM patients revealed that the proportion of AXL + cells in MGUS patients was higher than that in MM patients, while the number of AXL + cells in patients with recurrent disease was the least.This implies that the inactivation of AXL liberated cells from dormancy and facilitated MM cell proliferation, which may be associated with inferior survival outcomes.
In summary, researchers have used scRNA-seq to categorize immune cells such as NK and T cells into subpopulations with diverse roles at a greater resolution, illustrating the variety of traditional immune cell subgroups.4][175][176][177] In turn, the myeloma cells can shape a protective immune microenvironment by secreting cytokines to accumulate the regulatory immune cells and cytotoxic but impaired cells and interacting with myeloid components to assist their growth, expansion, and immune escape. 129,145

Targeted therapy
Over the past few decades, the overall survival rate of MM patients has remarkably improved due to advancements in autologous stem cell transplantation and novel drugs such as proteasome inhibitors (PIs), immunomodulatory drugs, and monoclonal antibodies. 178However, given the high heterogeneity of this disease, certain driver genes may be present in some patients but not others.
For KYDAR clinical trial patients or primary refractory MM (PRMM) patients, this study established a unique MM resistance pattern including stimulation of proteasome machinery (PSMB4 and PSMA2), mitochondrial stress (COX6C, COX7A2), ER and UPR pathway (PPIA, STMN1), as well as downregulation of PC checkpoint genes.The presence of this characteristic within a patient cohort is strongly correlated with an unfavorable prognosis.PPIA represents a major hallmark of the resistance signature and an ideal target for drug intervention.The combination of PIs and PPIA inhibitors may enhance the therapeutic efficacy of PIs, resulting in more effective elimination of tumor cells. 139From the perspective of transcriptional regulators, in addition to a set of regulations shared between malignant and normal plasma cells (including XBP1, PRDM1, and IRF4), aberrantly activated transcriptional regulators ELF3 and TEAD in MM may indicate widespread alterations in cis-regulatory regions, suggesting a loss of lineage restriction rather than defective differentiation of MM cells.This suggests that drugs can be used to inhibit the aberrant differentiation of MM cells to limit their malignancy.Moreover, therapy can modulate the activation of transcriptional regulators to regulate the expression of downstream genes and cell surface proteins, which may serve as potential targets for immunotherapy. 135urthermore, in recent years, chimeric antigen receptor (CAR) T-cell therapy targeting B-cell maturation antigen (BCMA) has demonstrated rapid and profound responses in treating MM/ RRMM.However, long-term maintained remissions are not achieved by most patients and relapse following therapy is frequently observed. 179,1802][183] A recent study found that MM relapse following CAR-T therapy may relate to the trogocytosis and internalization or biallelic loss of BCMA. 130,137,184ost of the current therapies target either tumor cells or immune cells, but inflammatory MSCs in the BM microenvironment remain unaffected by successful antitumor therapy.This may contribute to disease persistence or relapse and suggests a potential avenue for treating MM. 136 Finally, MM remains an incurable disease with a large number of relapsed/refractory patients.For patients with MM subsets that are already resistant before treatment, treatment will remove the inhibition of sensitive population, thus speeding up the growth of the resistant population, which may eventually worsen the disease. 185herefore, the development of personalized targeted drugs and identification of potential individualized therapeutic targets are crucial in the management of MM.

CONCLUSION
Since its inception in 2009, scRNA-seq has revolutionized our understanding of the intricate and diverse RNA expression patterns within individual cells.As technological advancements in cell capture techniques, sequencing throughput, and computational algorithms progress, we are gaining unprecedented insight into the complex biological mechanisms underlying celltype composition, GRNs, and cellular differentiation dynamics.With each new development, we are able to unlock increasingly intricate details about the fundamental biology of life at the cellular level.Numerous groups have applied scRNA-seq to the MM, it has been crucial in identifying new immune cell subsets, elucidating the degree of cellular malignancy in myeloma patients at different stages, and searching for mechanisms of drug resistance and relapse after treatment.After successful antitumor therapy, although myeloma cells have been effectively controlled, the presence of MRD and an inflammatory microenvironment in the BM may still predispose to disease recurrence.Our study covers the scRNA-seq platforms and analytic tools, which may be utilized to build a systematic research pipeline for future investigations.Simultaneously, we describe the key findings of prior scRNA-seq applications in MM, which may be utilized as a reference for prospective study.
diverse sample compatibility; cell size flexibility; rapid encapsulation of single cell; simple to operate High requirement of initial cell concentration and cell an open-source system; easily modified with different protocols; large crosssection channel without cell size bias in capture Low cell capturing efficiency (~7%); no analysis software support; skills to operate required open configurable system to develop new protocols and applications Low cell capturing efficiency (10%-20%); less suited for rare cells; low sensitivity of singlesize; sensitive and unbiased characterization of chromatin accessibility or transcriptional signatures No user's modification possible; extremely low cell capture

136 Da 147 Table 3 . 113 Fan
cells shape an immune-suppressive BME by accumulation of PD1 + γδ T cells and microphages the depletion of hematopoietic Initial CAR-T cell infusion yielded BCMA clones with biallelic deletion, resulting in a lack of CAR-T cell proliferation after the second the differential expression of AP-1 complex in plasma cell subsets; downstream targets of AP-1 (IL-6 and IL-1B) may modulate inflammatory NCOR1 and HDAC3 transcriptional co-repressors as the antagonists of P300/ CBP; the steady-state histone acetylation-methylation balance can control cellular transcription is higher in G2/M phase than in G1 or S phases in myeloma the selection of MRD clones in high-risk MM; supported undetectable MRD as a potential therapy endpoint 134 activated transcriptional regulators in MM show a loss of lineage restriction; drug treatment can induce the expression of the surface protein CXCL4 and serve as a potential the role of myeloma-specific inflammatory mesenchymal stromal cells in tumor survival and immune modulation; interferon-responsive effector T cell and CD8+ stem cell memory T-cell populations are potential sources of stromal cell-activating cytokines; successful antitumor induction therapy is unable to revert bone marrow inflammation the relationship between TNFRSF17 (BCMA) homozygous deletion clone and immune escape; heterozygous TNFRSF17 deletion theoretically correlates with BCMA loss after immunotherapy.model was presented in Vκ*MYC mice; it demonstrates a progressive activation pattern of a subclonal program associated with GCN2 stress response during the relevance of PPIA deletion or inhibition to the sensitivity of MM tumor cells to Indicated the role of adaptive NK cells in mediating ADCC in MM; selective use of adaptive NK cells can better predict and improve the therapeutic effect of perspective for normal PC development and transcriptional reorganization in AL and other monoclonal γ diseases.142Zavidij et al 2020 32; healthy donors (n = 9), MGUS (n = 5), low-risk SMM (n = 3), high-risk SMM (n = 8), MM (n = 7) Bone marrow Frozen CD45 + CD138− cells (n = 4463 from healthy donors, n = 2799 from MGUS, n = 1,782 from SMMl, n = 4,420 from SMMh, n = 5,563 from MM) 10× Genomics Chromium; Illumina HiSeq 2500/4000 Identified the immune changes that play a role in the evolution of premalignant Identified an important drug resistance mechanism; combination therapy with SIRT1 inhibitors can make myeloma cells sensitive to proteasome that transcriptional programs associated with the progression of invasive myeloma support autonomous cell proliferation and immune the role of S1PR2 downregulation in initial extramedullary translocation; S1PR2 downregulation promotes cell migration and invasion through NF-κB pathway and clinical significance of abnormal hematopoietic function in NDMM; MFC was moderately sensitive for screening abnormal hematopoietic function in NDMM the upregulation of CXCL12, a chemokine in circulating PC, may be related to the transfer of circulating PC from bone marrow to blood MM cells express a distinct transcriptome signature associated with myeloid cell lineage differentiation, cell survival and identifies new treatment targets the molecular pathways most significantly affected in MM progression; provided new features for predicting patient outcomes and stratifying of innate and adaptive immunity in premalignancy; found the mechanism of immune surveillance in myeloma consist of unique transcriptional signatures; identified the intratumor heterogeneity of individuals with multiple myeloma; revealed the transcriptional states correlation between CTCs and BM tumor transcriptional signatures relevant to cancer progression through HoneyBADGER; some prominent transcriptional subpopulations were likely driven by alternativean R Statistical analysis package and its application into clinically relevant analysis of intratumor heterogeneity; revealed the presence of pre-existing drugresistant subclones of cells inside untreated myeloma cells.the clinical potential of a noninvasive biopsy in MM; single CTCs have the same genetic abnormalities as BMPCs; revealed the importance of CTCs analysis in assessment for of 2-6 different major clones; the earliest myeloma-initiating clones only had the t (11;14); clonal diversity and selective pressures are essential foundation for tumor progression and treatment resistance in myeloma 155 AL = amyloidosis, BCMA = B-cell maturation antigen, BM = bone marrow, BMMC = bone marrow mononuclear cell, BME = bone marrow microenvironment, BR = b-resistant, CTC = circulating tumor cell, EMP = extramedullary plasmacytoma, HPC = hematopoietic progenitor cell, HMCLs = human myeloma cell lines, MFC = multidimensional flow cytometry, MGUS = monoclonal gammopathy of undetermined significance, MIF = migration inhibitory factor, MM = multiple myeloma, MRD = measurable residual disease, NDMM = newly diagnosed multiple myeloma, PB = peripheral blood, PBMC = peripheral blood mononuclear cell, PC = plasma cell, PD = progressive disease, PMCs = human primary myeloma cells, RRMM = relapsed/refractory multiple myeloma, SMM = smoldering multiple myeloma.

Figure 2 .
Figure 2. Advances of scRNA-seq in multiple myeloma.To date, single-cell RNA sequencing (scRNA-seq) has been applied to study the clonal evolution, bone marrow microenvironment, and targeted therapy of multiple myeloma.

Table 1 .
Moreover, although automatic annotation is

Table 3 .
Research progress of single-cell RNA sequencing in MM.
128Tirier et al